Samuel J. Harrison

Multiscale Modes of Functional Brain Connectivity

This paper introduces mPFMs, a new method for multiscale functional brain connectivity analysis that improves prediction of UK Biobank traits compared to standard techniques.

Comparison Between Gradients and Parcellations for Functional Connectivity Prediction of Behavior

This study compares parcellation and gradient methods for predicting behavior from resting-state functional connectivity in two datasets, finding that individual-specific hard parcellations perform best in HCP and principal gradients perform similarly in ABCD.

Interoception of breathing and its relationship with anxiety

This study investigates how anxiety relates to interoceptive processing of breathing using fMRI and computational models, finding that anxiety traits affect interoceptive prediction certainty and errors in the anterior insula.

The Filter Detection Task for measurement of breathing-related interoception and metacognition

The paper introduces a task and analysis procedure to quantify breathing-related interoception dimensions using simulated and empirical data.

Hierarchical modelling of functional brain networks in population and individuals from big fMRI data

This paper introduces sPROFUMO, a hierarchical model for estimating functional brain networks in individuals and populations from large fMRI datasets, showing improved performance over traditional methods.

TAPAS: an open-source software package for Translational Neuromodeling and Computational Psychiatry

TAPAS provides open-source tools for computational assays in psychiatry to support differential diagnosis and treatment prediction through end-to-end pipelines.

Regression dynamic causal modeling for resting-state fMRI

This paper introduces regression dynamic causal modeling (rDCM) for resting-state fMRI, providing directional connectivity estimates and scalability to whole-brain networks.

A Hilbert-based method for processing respiratory timeseries

This paper introduces a Hilbert-based method to estimate respiratory volume per unit time from bellows recordings, improving fMRI noise correction by better capturing atypical breathing events.

The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants.

This paper introduces an automated pipeline for processing neonatal resting-state fMRI data, improving motion correction and quality assurance in the developing Human Connectome Project.

Modelling Subject Variability in the Spatial and Temporal Characteristics of Functional Modes

The paper extends probabilistic functional modes to model subject variability in fMRI data, showing spatial maps capture most cross-subject variability and functional coupling reflects cognitive state.

The relationship between spatial configuration and functional connectivity of brain regions revisited

The paper revisits the relationship between spatial configuration and functional connectivity, showing spatial overlap biases connectivity estimates and introducing an improved method to address this bias.

Using Temporal ICA to Selectively Remove Global Noise While Preserving Global Signal in Functional MRI Data

This paper introduces temporal ICA to remove global structured noise from fMRI data while preserving global neural signals, improving upon traditional global signal regression methods.

The relationship between spatial configuration and functional connectivity of brain regions

This study examines how the spatial arrangement of brain regions influences functional connectivity and behavioral predictions.